Analysis of GLDS-7 from NASA GeneLab

This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven

Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491

1. Read data

First we set up the working directory to where the files are saved.

 setwd('~/Documents/HTML_R/GLDS7')

R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.

 if(file.exists('iDEP_core_functions.R'))
    source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).

 inputFile <- 'GLDS7_Expression.csv'
 sampleInfoFile <- 'GLDS7_Sampleinfo.csv'
 gldsMetadataFile <- 'GLDS7_Metadata.csv'
 geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
 geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db'  # pathway database in SQL; can be GMT format 
 STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

 input_missingValue <- 'geneMedian' #Missing values imputation method
 input_dataFileFormat <- 1  #1- read counts, 2 FKPM/RPKM or DNA microarray
 input_minCounts <- 0.5 #Min counts
 input_NminSamples <- 1 #Minimum number of samples 
 input_countsLogStart <- 4  #Pseudo count for log CPM
 input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr)   #  install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Hypocotyl_FLT_Rep1 Hypocotyl_FLT_Rep2 Hypocotyl_FLT_Rep3 Hypocotyl_FLT_Rep4 Hypocotyl_FLT_Rep5 Hypocotyl_GC_Rep1 Hypocotyl_GC_Rep2 Hypocotyl_GC_Rep3 Hypocotyl_GC_Rep4 Hypocotyl_GC_Rep5 Root_FLT_Rep1 Root_FLT_Rep2 Root_FLT_Rep3 Root_FLT_Rep4 Root_FLT_Rep5 Root_GC_Rep1 Root_GC_Rep2 Root_GC_Rep3 Root_GC_Rep4 Root_GC_Rep5 Shoot_FLT_Rep1 Shoot_FLT_Rep2 Shoot_FLT_Rep3 Shoot_FLT_Rep4 Shoot_FLT_Rep5 Shoot_GC_Rep1 Shoot_GC_Rep2 Shoot_GC_Rep3 Shoot_GC_Rep4 Shoot_GC_Rep5 WholeSeedling_FLT_Rep1 WholeSeedling_FLT_Rep2 WholeSeedling_FLT_Rep3 WholeSeedling_GC_Rep1 WholeSeedling_GC_Rep2 WholeSeedling_GC_Rep3
Sample.LongId Atha.WS.0.Col.0.Hypocotyl.FLT.Rep1.Array Atha.WS.0.Col.0.Hypocotyl.FLT.Rep2.Array Atha.WS.0.Col.0.Hypocotyl.FLT.Rep3.Array Atha.WS.0.Col.0.Hypocotyl.FLT.Rep4.Array Atha.WS.0.Col.0.Hypocotyl.FLT.Rep5.Array Atha.WS.0.Col.0.Hypocotyl.GC.Rep1.Array Atha.WS.0.Col.0.Hypocotyl.GC.Rep2.Array Atha.WS.0.Col.0.Hypocotyl.GC.Rep3.Array Atha.WS.0.Col.0.Hypocotyl.GC.Rep4.Array Atha.WS.0.Col.0.Hypocotyl.GC.Rep5.Array Atha.WS.0.Col.0.Root.FLT.Rep1.Array Atha.WS.0.Col.0.Root.FLT.Rep2.Array Atha.WS.0.Col.0.Root.FLT.Rep3.Array Atha.WS.0.Col.0.Root.FLT.Rep4.Array Atha.WS.0.Col.0.Root.FLT.Rep5.Array Atha.WS.0.Col.0.Root.GC.Rep1.Array Atha.WS.0.Col.0.Root.GC.Rep2.Array Atha.WS.0.Col.0.Root.GC.Rep3.Array Atha.WS.0.Col.0.Root.GC.Rep4.Array Atha.WS.0.Col.0.Root.GC.Rep5.Array Atha.WS.0.Col.0.Shoot.FLT.Rep1.Array Atha.WS.0.Col.0.Shoot.FLT.Rep2.Array Atha.WS.0.Col.0.Shoot.FLT.Rep3.Array Atha.WS.0.Col.0.Shoot.FLT.Rep4.Array Atha.WS.0.Col.0.Shoot.FLT.Rep5.Array Atha.WS.0.Col.0.Shoot.GC.Rep1.Array Atha.WS.0.Col.0.Shoot.GC.Rep2.Array Atha.WS.0.Col.0.Shoot.GC.Rep3.Array Atha.WS.0.Col.0.Shoot.GC.Rep4.Array Atha.WS.0.Col.0.Shoot.GC.Rep5.Array Atha.WS.0.Whole.Plant.FLT.Rep1.Array Atha.WS.0.Whole.Plant.FLT.Rep2.Array Atha.WS.0.Whole.Plant.FLT.Rep3.Array Atha.WS.0.Whole.Plant.GC.Rep1.Array Atha.WS.0.Whole.Plant.GC.Rep2.Array Atha.WS.0.Whole.Plant.GC.Rep3.Array
Sample.Id Atha.WS.0.Col.0.Hypocotyl.FLT.Rep1 Atha.WS.0.Col.0.Hypocotyl.FLT.Rep2 Atha.WS.0.Col.0.Hypocotyl.FLT.Rep3 Atha.WS.0.Col.0.Hypocotyl.FLT.Rep4 Atha.WS.0.Col.0.Hypocotyl.FLT.Rep5 Atha.WS.0.Col.0.Hypocotyl.GC.Rep1 Atha.WS.0.Col.0.Hypocotyl.GC.Rep2 Atha.WS.0.Col.0.Hypocotyl.GC.Rep3 Atha.WS.0.Col.0.Hypocotyl.GC.Rep4 Atha.WS.0.Col.0.Hypocotyl.GC.Rep5 Atha.WS.0.Col.0.Root.FLT.Rep1 Atha.WS.0.Col.0.Root.FLT.Rep2 Atha.WS.0.Col.0.Root.FLT.Rep3 Atha.WS.0.Col.0.Root.FLT.Rep4 Atha.WS.0.Col.0.Root.FLT.Rep5 Atha.WS.0.Col.0.Root.GC.Rep1 Atha.WS.0.Col.0.Root.GC.Rep2 Atha.WS.0.Col.0.Root.GC.Rep3 Atha.WS.0.Col.0.Root.GC.Rep4 Atha.WS.0.Col.0.Root.GC.Rep5 Atha.WS.0.Col.0.Shoot.FLT.Rep1 Atha.WS.0.Col.0.Shoot.FLT.Rep2 Atha.WS.0.Col.0.Shoot.FLT.Rep3 Atha.WS.0.Col.0.Shoot.FLT.Rep4 Atha.WS.0.Col.0.Shoot.FLT.Rep5 Atha.WS.0.Col.0.Shoot.GC.Rep1 Atha.WS.0.Col.0.Shoot.GC.Rep2 Atha.WS.0.Col.0.Shoot.GC.Rep3 Atha.WS.0.Col.0.Shoot.GC.Rep4 Atha.WS.0.Col.0.Shoot.GC.Rep5 Atha.WS.0.Whole.Plant.FLT.Rep1 Atha.WS.0.Whole.Plant.FLT.Rep2 Atha.WS.0.Whole.Plant.FLT.Rep3 Atha.WS.0.Whole.Plant.GC.Rep1 Atha.WS.0.Whole.Plant.GC.Rep2 Atha.WS.0.Whole.Plant.GC.Rep3
Sample.Name Atha_WS-0_Col-0_Hypocotyl_FLT_Rep1 Atha_WS-0_Col-0_Hypocotyl_FLT_Rep2 Atha_WS-0_Col-0_Hypocotyl_FLT_Rep3 Atha_WS-0_Col-0_Hypocotyl_FLT_Rep4 Atha_WS-0_Col-0_Hypocotyl_FLT_Rep5 Atha_WS-0_Col-0_Hypocotyl_GC_Rep1 Atha_WS-0_Col-0_Hypocotyl_GC_Rep2 Atha_WS-0_Col-0_Hypocotyl_GC_Rep3 Atha_WS-0_Col-0_Hypocotyl_GC_Rep4 Atha_WS-0_Col-0_Hypocotyl_GC_Rep5 Atha_WS-0_Col-0_Root_FLT_Rep1 Atha_WS-0_Col-0_Root_FLT_Rep2 Atha_WS-0_Col-0_Root_FLT_Rep3 Atha_WS-0_Col-0_Root_FLT_Rep4 Atha_WS-0_Col-0_Root_FLT_Rep5 Atha_WS-0_Col-0_Root_GC_Rep1 Atha_WS-0_Col-0_Root_GC_Rep2 Atha_WS-0_Col-0_Root_GC_Rep3 Atha_WS-0_Col-0_Root_GC_Rep4 Atha_WS-0_Col-0_Root_GC_Rep5 Atha_WS-0_Col-0_Shoot_FLT_Rep1 Atha_WS-0_Col-0_Shoot_FLT_Rep2 Atha_WS-0_Col-0_Shoot_FLT_Rep3 Atha_WS-0_Col-0_Shoot_FLT_Rep4 Atha_WS-0_Col-0_Shoot_FLT_Rep5 Atha_WS-0_Col-0_Shoot_GC_Rep1 Atha_WS-0_Col-0_Shoot_GC_Rep2 Atha_WS-0_Col-0_Shoot_GC_Rep3 Atha_WS-0_Col-0_Shoot_GC_Rep4 Atha_WS-0_Col-0_Shoot_GC_Rep5 Atha_WS-0_Whole-Plant_FLT_Rep1 Atha_WS-0_Whole-Plant_FLT_Rep2 Atha_WS-0_Whole-Plant_FLT_Rep3 Atha_WS-0_Whole-Plant_GC_Rep1 Atha_WS-0_Whole-Plant_GC_Rep2 Atha_WS-0_Whole-Plant_GC_Rep3
GLDS 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Accession GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7 GLDS-7
Hardware ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS ABRS
Tissue Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Hypocotyl Roots Roots Roots Roots Roots Roots Roots Roots Roots Roots Shoot Shoot Shoot Shoot Shoot Shoot Shoot Shoot Shoot Shoot Whole seedling Whole seedling Whole seedling Whole seedling Whole seedling Whole seedling
Age 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days 12 days
Organism Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana
Ecotype Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed Col-0 & WS-0 mixed
Genotype WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT WT
Variety Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT Col-0 & WS-0 mixed WT
Radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth Background Earth Background Earth Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth Background Earth Background Earth Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth Background Earth Background Earth Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth
Gravity Microgravity Microgravity Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Microgravity Microgravity Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Microgravity Microgravity Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial
Developmental 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling hypocotyl 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling shoots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots 12 day old seedling roots
Time.series.or.Concentration.gradient Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point
Light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light White light
Assay..RNAseq. Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling
Temperature 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24
Treatment.type Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight Space flight
Treatment.intensity x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
Treament.timing x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
Preservation.Method. RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater RNALater 
 readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
   kable( head(readData.out$data) ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Hypocotyl_FLT_Rep1 Hypocotyl_FLT_Rep2 Hypocotyl_FLT_Rep3 Hypocotyl_FLT_Rep4 Hypocotyl_FLT_Rep5 Hypocotyl_GC_Rep1 Hypocotyl_GC_Rep2 Hypocotyl_GC_Rep3 Hypocotyl_GC_Rep4 Hypocotyl_GC_Rep5 Root_FLT_Rep1 Root_FLT_Rep2 Root_FLT_Rep3 Root_FLT_Rep4 Root_FLT_Rep5 Root_GC_Rep1 Root_GC_Rep2 Root_GC_Rep3 Root_GC_Rep4 Root_GC_Rep5 Shoot_FLT_Rep1 Shoot_FLT_Rep2 Shoot_FLT_Rep3 Shoot_FLT_Rep4 Shoot_FLT_Rep5 Shoot_GC_Rep1 Shoot_GC_Rep2 Shoot_GC_Rep3 Shoot_GC_Rep4 Shoot_GC_Rep5 WholeSeedling_FLT_Rep1 WholeSeedling_FLT_Rep2 WholeSeedling_FLT_Rep3 WholeSeedling_GC_Rep1 WholeSeedling_GC_Rep2 WholeSeedling_GC_Rep3
AT3G01500 2.797288 2.801275 2.801971 2.801092 2.801110 2.577317 2.798415 2.800664 2.795681 2.799021 2.800339 2.575584 2.794532 2.802569 2.579733 2.575710 2.800731 2.798693 2.801759 2.804228 3.911239 3.920361 3.909440 3.913533 3.807355 3.927754 3.915216 3.812816 3.812959 3.813825 3.715466 3.722478 3.607228 3.715466 3.723016 3.718766
AT5G14740 2.797288 2.992904 2.801971 2.801092 2.801110 2.797518 2.798415 2.800664 2.575891 2.799021 2.800339 2.575584 2.574999 2.802569 2.579733 2.795448 2.800731 2.798693 2.801759 2.804228 3.811591 3.920361 3.809838 3.813825 3.807355 3.827680 3.815465 3.812816 3.812959 3.813825 3.599435 3.722478 3.607228 3.715466 3.606709 3.718766
AT5G46890 2.577138 2.580236 2.580777 2.580094 2.580108 2.577317 2.578014 2.579761 2.575891 2.578485 3.795643 3.787183 3.679669 3.799369 3.796125 3.787455 3.796299 3.792889 3.897302 3.901536 2.809898 3.180141 2.808845 2.587985 2.584963 2.594483 2.812226 2.587513 2.810720 2.587985 3.181995 3.341048 3.341982 3.181995 3.480197 3.476286
AT2G10940 2.797288 2.801275 2.801971 2.580094 2.580108 2.577317 2.578014 2.800664 2.795681 2.799021 2.579509 2.575584 2.574999 2.581241 2.800627 2.575710 2.800731 2.578230 2.801759 2.804228 3.811591 3.820477 3.809838 3.813825 3.700440 3.827680 3.815465 3.812816 3.812959 3.813825 3.599435 3.606191 3.607228 3.599435 3.606709 3.602614
AT1G09310 3.684142 3.797207 3.691732 3.690310 3.690339 3.684516 3.685971 3.689616 3.681535 3.571978 2.800339 2.985909 2.985029 3.163717 2.800627 2.795448 2.800731 2.989889 2.993469 2.996351 4.004447 4.013775 4.002607 4.006793 4.000000 3.927754 4.008514 4.005733 4.005884 4.006793 4.016271 4.023860 4.025025 4.016271 4.024442 4.019843
ATCG00680 3.156861 3.162039 2.801971 2.992691 2.801110 3.157161 2.989565 2.992191 2.986371 2.990273 2.800339 2.795285 2.794532 2.802569 2.800627 2.795448 2.800731 2.798693 2.801759 2.804228 3.463206 3.713160 3.461644 3.591002 3.321928 3.827680 3.708301 3.464297 3.705872 3.465197 4.016271 4.023860 4.025025 4.016271 4.024442 4.019843 
 readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
 kable( readSampleInfo.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Gravity Tissue
Hypocotyl_FLT_Rep1 Microgravity Hypocotyl
Hypocotyl_FLT_Rep2 Microgravity Hypocotyl
Hypocotyl_FLT_Rep3 Microgravity Hypocotyl
Hypocotyl_FLT_Rep4 Microgravity Hypocotyl
Hypocotyl_FLT_Rep5 Microgravity Hypocotyl
Hypocotyl_GC_Rep1 Terrestrial Hypocotyl
Hypocotyl_GC_Rep2 Terrestrial Hypocotyl
Hypocotyl_GC_Rep3 Terrestrial Hypocotyl
Hypocotyl_GC_Rep4 Terrestrial Hypocotyl
Hypocotyl_GC_Rep5 Terrestrial Hypocotyl
Root_FLT_Rep1 Microgravity Roots
Root_FLT_Rep2 Microgravity Roots
Root_FLT_Rep3 Microgravity Roots
Root_FLT_Rep4 Microgravity Roots
Root_FLT_Rep5 Microgravity Roots
Root_GC_Rep1 Terrestrial Roots
Root_GC_Rep2 Terrestrial Roots
Root_GC_Rep3 Terrestrial Roots
Root_GC_Rep4 Terrestrial Roots
Root_GC_Rep5 Terrestrial Roots
Shoot_FLT_Rep1 Microgravity Shoot
Shoot_FLT_Rep2 Microgravity Shoot
Shoot_FLT_Rep3 Microgravity Shoot
Shoot_FLT_Rep4 Microgravity Shoot
Shoot_FLT_Rep5 Microgravity Shoot
Shoot_GC_Rep1 Terrestrial Shoot
Shoot_GC_Rep2 Terrestrial Shoot
Shoot_GC_Rep3 Terrestrial Shoot
Shoot_GC_Rep4 Terrestrial Shoot
Shoot_GC_Rep5 Terrestrial Shoot
WholeSeedling_FLT_Rep1 Microgravity WholeSeedling
WholeSeedling_FLT_Rep2 Microgravity WholeSeedling
WholeSeedling_FLT_Rep3 Microgravity WholeSeedling
WholeSeedling_GC_Rep1 Terrestrial WholeSeedling
WholeSeedling_GC_Rep2 Terrestrial WholeSeedling
WholeSeedling_GC_Rep3 Terrestrial WholeSeedling 
 input_selectOrg ="NEW" 
 input_selectGO <- 'GOBP'   #Gene set category 
 input_noIDConversion = TRUE  
 allGeneInfo.out <- geneInfo(geneInfoFile) 
 converted.out = NULL 
 convertedData.out <- convertedData()    
 nGenesFilter()  
## [1] "16156 genes in 36 samples. 16156  genes passed filter.\n Original gene IDs used."
 convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
 parDefault = par() 
 par(mar=c(12,4,2,2)) 
 # barplot of total read counts
 x <- readData.out$rawCounts
 groups = as.factor( detectGroups(colnames(x ) ) )
 if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
  col1 = rainbow(nlevels(groups))[ groups ]             
         
 barplot( colSums(x)/1e6, 
        col=col1,las=3, main="Total read counts (millions)")  

 readCountsBias()  # detecting bias in sequencing depth 
## [1] 5.679193e-14
## [1] 0.842966
## [1] 1.824797e-16
## [1] "Warning! Sequencing depth bias detected. Total read counts are significantly different among sample groups (p= 5.68e-14 ) based on ANOVA.  Total read counts seem to be correlated with factor Tissue (p= 1.82e-16 ).  "
 # Box plot 
 x = readData.out$data 
 boxplot(x, las = 2, col=col1,
    ylab='Transformed expression levels',
    main='Distribution of transformed data') 

 #Density plot 
 par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
 densityPlot()       

 # Scatter plot of the first two samples 
 plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
    main='Scatter plot of first two samples') 

 ####plot gene or gene family
 input_selectOrg ="BestMatch" 
 input_geneSearch <- 'HOXA' #Gene ID for searching 
 genePlot()  
## NULL
 input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar? 
 geneBarPlotError()       
## NULL

3. Heatmap

 # hierarchical clustering tree
 x <- readData.out$data
 maxGene <- apply(x,1,max)
 # remove bottom 25% lowly expressed genes, which inflate the PPC
 x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
 plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

 #Correlation matrix
 input_labelPCC <- TRUE #Show correlation coefficient? 
 correlationMatrix() 

 # Parameters for heatmap
 input_nGenes <- 1000   #Top genes for heatmap
 input_geneCentering <- TRUE    #centering genes ?
 input_sampleCentering <- FALSE #Center by sample?
 input_geneNormalize <- FALSE   #Normalize by gene?
 input_sampleNormalize <- FALSE #Normalize by sample?
 input_noSampleClustering <- FALSE  #Use original sample order
 input_heatmapCutoff <- 4   #Remove outliers beyond number of SDs 
 input_distFunctions <- 1   #which distant funciton to use
 input_hclustFunctions <- 1 #Linkage type
 input_heatColors1 <- 1 #Colors
 input_selectFactorsHeatmap <- 'Gravity'    #Sample coloring factors 
 png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
 staticHeatmap() 
 dev.off()  
## png 
##   2

[heatmap] (heatmap.png)

 heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

 input_nGenesKNN <- 2000    #Number of genes fro k-Means
 input_nClusters <- 4   #Number of clusters 
 maxGeneClustering = 12000
 input_kmeansNormalization <- 'geneMean'    #Normalization
 input_KmeansReRun <- 0 #Random seed 

 distributionSD()  #Distribution of standard deviations 

 KmeansNclusters()  #Number of clusters 

 Kmeans.out = Kmeans()   #Running K-means 
 KmeansHeatmap()   #Heatmap for k-Means 

 #Read gene sets for enrichment analysis 
 sqlite  <- dbDriver('SQLite')
 input_selectGO3 <- 'GOBP'  #Gene set category
 input_minSetSize <- 15 #Min gene set size
 input_maxSetSize <- 2000   #Max gene set size 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO3,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 # Alternatively, users can use their own GMT files by
 #GeneSets.out <- readGMTRobust('somefile.GMT')  
 results <- KmeansGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 3.75e-139 124 Photosynthesis
1.43e-90 76 Photosynthesis, light reaction
4.31e-60 91 Generation of precursor metabolites and energy
4.60e-50 165 Response to abiotic stimulus
3.48e-43 34 Photosynthetic electron transport chain
6.49e-37 83 Response to light stimulus
4.21e-36 118 Oxidation-reduction process
1.01e-35 83 Response to radiation
3.19e-33 124 Organonitrogen compound biosynthetic process
1.32e-30 52 Plastid organization
B 3.19e-08 50 Nucleobase-containing compound biosynthetic process
3.68e-08 46 Regulation of nucleobase-containing compound metabolic process
1.15e-07 41 Regulation of transcription, DNA-templated
1.15e-07 47 Regulation of gene expression
1.15e-07 43 Regulation of RNA metabolic process
1.15e-07 41 Regulation of nucleic acid-templated transcription
1.15e-07 41 Regulation of RNA biosynthetic process
1.27e-07 42 Transcription, DNA-templated
1.33e-07 42 Nucleic acid-templated transcription
1.57e-07 42 RNA biosynthetic process
C 6.87e-19 29 Cellular response to decreased oxygen levels
6.87e-19 72 Cellular response to chemical stimulus
6.87e-19 29 Cellular response to oxygen levels
6.87e-19 29 Cellular response to hypoxia
8.23e-19 86 Response to abiotic stimulus
1.18e-17 29 Response to hypoxia
1.56e-17 29 Response to decreased oxygen levels
1.57e-17 29 Response to oxygen levels
2.58e-15 76 Response to organic substance
2.58e-15 67 Response to oxygen-containing compound
D 3.93e-32 43 Detoxification
5.52e-28 47 Response to toxic substance
1.40e-24 43 Secondary metabolic process
2.83e-23 33 Cellular response to toxic substance
3.32e-23 86 Oxidation-reduction process
3.39e-23 26 Antibiotic catabolic process
1.25e-22 24 Hydrogen peroxide catabolic process
2.77e-22 31 Cellular detoxification
4.94e-21 25 Hydrogen peroxide metabolic process
6.91e-21 29 Cellular oxidant detoxification 
 input_seedTSNE <- 0    #Random seed for t-SNE
 input_colorGenes <- TRUE   #Color genes in t-SNE plot? 
 tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

 input_selectFactors <- 'Gravity'   #Factor coded by color
 input_selectFactors2 <- 'Tissue'   #Factor coded by shape
 input_tsneSeed2 <- 0   #Random seed for t-SNE 
 #PCA, MDS and t-SNE plots
 PCAplot()  

 MDSplot() 

 tSNEplot()  

 #Read gene sets for pathway analysis using PGSEA on principal components 
 input_selectGO6 <- 'GOBP' 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO6,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

 cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Tissue (p=4.10e-31).
## PC2 is correlated with Tissue (p=2.89e-36).
## PC3 is correlated with Tissue (p=1.83e-25).
## PC4 is correlated with Gravity (p=7.19e-05).

6. DEG1

 input_CountsDEGMethod <- 2 #DESeq2= 3,limma-voom=2,limma-trend=1 
 input_limmaPval <- 0.1 #FDR cutoff
 input_limmaFC <- 2 #Fold-change cutoff
 input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial'  #Selected comparisons
 input_selectFactorsModel <- 'Gravity'  #Selected comparisons
 input_selectInteractions <- NULL   #Selected comparisons
 input_selectBlockFactorsModel <- NULL  #Selected comparisons 
 factorReferenceLevels.out <- NULL 

 limma.out <- limma()
 DEG.data.out <- DEG.data()
 limma.out$comparisons 
## [1] "Microgravity-Terrestrial"
 input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
 input_UpDownRegulated <- FALSE #Split up and down regulated genes 
 vennPlot() # Venn diagram 

  sigGeneStats() # number of DEGs as figure 

  sigGeneStatsTable() # number of DEGs as table 
##                                       Comparisons Up Down
## Microgravity-Terrestrial Microgravity-Terrestrial  0    0

7. DEG2

 input_selectContrast <- 'Terrestrial-Microgravity' #Selected comparisons 
 selectedHeatmap.data.out <- selectedHeatmap.data()
 selectedHeatmap()   # heatmap for DEGs in selected comparison
## Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'
 # Save gene lists and data into files
 write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
 write.csv(DEG.data(),'DEG.data.csv' )
 write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
 input_selectGO2 <- 'GOBP'  #Gene set category 
 geneListData.out <- geneListData()  
 volcanoPlot()  

  scatterPlot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  MAplot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  geneListGOTable.out <- geneListGOTable()  
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO2,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_removeRedudantSets <- TRUE   #Remove highly redundant gene sets? 
 results <- geneListGO()  #Enrichment analysis
## Error in if (dim(results1)[2] == 1) return(results1) else {: argument is of length zero
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 3.75e-139 124 Photosynthesis
1.43e-90 76 Photosynthesis, light reaction
4.31e-60 91 Generation of precursor metabolites and energy
4.60e-50 165 Response to abiotic stimulus
3.48e-43 34 Photosynthetic electron transport chain
6.49e-37 83 Response to light stimulus
4.21e-36 118 Oxidation-reduction process
1.01e-35 83 Response to radiation
3.19e-33 124 Organonitrogen compound biosynthetic process
1.32e-30 52 Plastid organization
B 3.19e-08 50 Nucleobase-containing compound biosynthetic process
3.68e-08 46 Regulation of nucleobase-containing compound metabolic process
1.15e-07 41 Regulation of transcription, DNA-templated
1.15e-07 47 Regulation of gene expression
1.15e-07 43 Regulation of RNA metabolic process
1.15e-07 41 Regulation of nucleic acid-templated transcription
1.15e-07 41 Regulation of RNA biosynthetic process
1.27e-07 42 Transcription, DNA-templated
1.33e-07 42 Nucleic acid-templated transcription
1.57e-07 42 RNA biosynthetic process
C 6.87e-19 29 Cellular response to decreased oxygen levels
6.87e-19 72 Cellular response to chemical stimulus
6.87e-19 29 Cellular response to oxygen levels
6.87e-19 29 Cellular response to hypoxia
8.23e-19 86 Response to abiotic stimulus
1.18e-17 29 Response to hypoxia
1.56e-17 29 Response to decreased oxygen levels
1.57e-17 29 Response to oxygen levels
2.58e-15 76 Response to organic substance
2.58e-15 67 Response to oxygen-containing compound
D 3.93e-32 43 Detoxification
5.52e-28 47 Response to toxic substance
1.40e-24 43 Secondary metabolic process
2.83e-23 33 Cellular response to toxic substance
3.32e-23 86 Oxidation-reduction process
3.39e-23 26 Antibiotic catabolic process
1.25e-22 24 Hydrogen peroxide catabolic process
2.77e-22 31 Cellular detoxification
4.94e-21 25 Hydrogen peroxide metabolic process
6.91e-21 29 Cellular oxidant detoxification

STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.

 STRING10_species = read.csv(STRING10_speciesFile)  
 ix = grep('Arabidopsis thaliana', STRING10_species$official_name ) 
 findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
 findTaxonomyID.out  
## [1] 3702

Enrichment analysis using STRING

  STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in names(x) <- value: 'names' attribute [2] must be the same length as the vector [1]
 input_STRINGdbGO <- 'Process'  #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro' 
 results <- stringDB_GO_enrichmentData()  # enrichment using STRING 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
x
NULL

PPI network retrieval and analysis

 input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis 
 stringDB_network1(1) #Show PPI network 
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found

Generating interactive PPI

 write(stringDB_network_link(), 'PPI_results.html') # write results to html file 
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
 browseURL('PPI_results.html') # open in browser

8. Pathway analysis

 input_selectContrast1 <- 'Terrestrial-Microgravity'    #select Comparison 
 #input_selectContrast1 = limma.out$comparisons[3] # manually set
 input_selectGO <- 'GOBP'   #Gene set category 
 #input_selectGO='custom' # if custom gmt file
 input_minSetSize <- 15 #Min size for gene set
 input_maxSetSize <- 2000   #Max size for gene set 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_pathwayPvalCutoff <- 0.2 #FDR cutoff
 input_nPathwayShow <- 30   #Top pathways to show
 input_absoluteFold <- FALSE    #Use absolute values of fold-change?
 input_GenePvalCutoff <- 1  #FDR to remove genes 

 input_pathwayMethod = 1  # 1  GAGE
 gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  
   
 results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters  
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GAGE analysis: Terrestrial vs Microgravity statistic Genes adj.Pval
Up Cellular response to decreased oxygen levels 10.127 176 5.4e-18
Cellular response to oxygen levels 10.127 176 5.4e-18
Cellular response to hypoxia 10.0985 175 5.4e-18
Response to hypoxia 9.6969 197 3.0e-17
Response to decreased oxygen levels 9.6906 200 3.0e-17
Response to oxygen levels 9.6474 201 3.2e-17
Response to chitin 7.6128 104 4.5e-10
Response to drug 7.0437 482 4.9e-10
Response to organonitrogen compound 5.8554 212 1.2e-06
Response to nitrogen compound 5.4244 271 9.3e-06
Immune system process 5.1485 345 3.0e-05
Response to wounding 4.9489 151 1.0e-04
Immune response 4.9133 312 9.0e-05
Response to bacterium 4.83 400 1.1e-04
Response to antibiotic 4.8046 255 1.3e-04
Innate immune response 4.6716 303 2.2e-04
Defense response to bacterium 4.6489 345 2.2e-04
Ethylene-activated signaling pathway 4.3021 141 1.3e-03
Response to ethylene 4.0945 227 2.6e-03
Regulation of defense response 4.0017 218 3.5e-03
Regulation of response to stress 3.9519 322 3.8e-03
Response to organic cyclic compound 3.948 291 3.8e-03
Response to temperature stimulus 3.8799 493 4.5e-03
Response to salicylic acid 3.8412 163 5.5e-03
Response to heat 3.8272 188 5.5e-03
Positive regulation of cellular biosynthetic process 3.8163 457 5.5e-03
Positive regulation of biosynthetic process 3.7696 468 6.0e-03
Positive regulation of macromolecule biosynthetic process 3.7372 435 6.6e-03
Positive regulation of nucleobase-containing compound metabolic process 3.7037 445 7.1e-03
Positive regulation of RNA metabolic process 3.703 412 7.1e-03 
 pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
 input_pathwayMethod = 3  # 1  fgsea 
 fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
 results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GSEA analysis: Terrestrial vs Microgravity NES Genes adj.Pval
Up Cellular response to hypoxia 2.9882 175 3.3e-03
Cellular response to decreased oxygen levels 2.981 176 3.3e-03
Cellular response to oxygen levels 2.981 176 3.3e-03
Response to hypoxia 2.9627 197 3.3e-03
Response to decreased oxygen levels 2.9371 200 3.3e-03
Response to oxygen levels 2.933 201 3.3e-03
Response to chitin 2.8501 104 3.4e-03
Response to organonitrogen compound 2.5552 212 3.3e-03
Response to drug 2.4517 482 3.3e-03
Response to nitrogen compound 2.3891 271 3.3e-03
Response to wounding 2.3397 151 3.3e-03
Ethylene-activated signaling pathway 2.2296 141 3.3e-03
Response to antibiotic 2.2239 255 3.3e-03
Defense response to bacterium, incompatible interaction 2.1765 40 3.5e-03
Response to salicylic acid 2.1524 163 3.3e-03
Cellular response to ethylene stimulus 2.0978 157 3.3e-03
Immune system process 2.0893 345 3.3e-03
Response to ethylene 2.0884 227 3.3e-03
Response to hydrogen peroxide 2.0764 65 3.4e-03
Regulation of secondary metabolic process 2.0661 39 6.1e-03
Immune response 2.0583 312 3.3e-03
Response to jasmonic acid 2.0554 179 3.3e-03
Defense response to other organism 2.0504 598 3.3e-03
Phosphorelay signal transduction system 2.0367 181 3.3e-03
Regulation of DNA-templated transcription in response to stress 2.0314 25 3.6e-03
Defense response to bacterium 2.0279 345 3.3e-03
Defense response 2.0218 936 3.3e-03
Response to bacterium 2.0191 400 3.3e-03
Innate immune response 2.0171 303 3.3e-03
Regulation of defense response 2.0154 218 3.3e-03 
  pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

   PGSEAplot() # pathway analysis using PGSEA 
## Error in findContrastSamples(input_selectContrast1, colnames(convertedData.out), : object 'c.out' not found

9. Chromosome

 input_selectContrast2 <- 'Terrestrial-Microgravity'    #select Comparison 
 #input_selectContrast2 = limma.out$comparisons[3] # manually set
 input_limmaPvalViz <- 0.1  #FDR to filter genes
 input_limmaFCViz <- 2  #FDR to filter genes 
 genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion

10. Biclustering

 input_nGenesBiclust <- 1000    #Top genes for biclustering
 input_biclustMethod <- 'BCCC()'    #Method: 'BCCC', 'QUBIC', 'runibic' ... 
 biclustering.out = biclustering()  # run analysis

 input_selectBicluster <- 1 #select a cluster 
 biclustHeatmap()   # heatmap for selected cluster 

 input_selectGO4 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO4,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.4e-83 100 Photosynthesis
2.7e-55 171 Oxidation-reduction process
1.9e-50 58 Photosynthesis, light reaction
9.4e-40 83 Generation of precursor metabolites and energy
1.8e-31 167 Response to abiotic stimulus
7.1e-26 27 Photosynthetic electron transport chain
1.4e-25 46 Electron transport chain
3.5e-24 43 Drug catabolic process
6.1e-24 82 Drug metabolic process
8.5e-24 24 Protein-chromophore linkage

11. Co-expression network

 input_mySoftPower <- 5 #SoftPower to cutoff
 input_nGenesNetwork <- 1000    #Number of top genes
 input_minModuleSize <- 20  #Module size minimum 
 wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq    slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.859000  2.92000         0.8180   576.0     615.0  722.0
## 2      2 0.870000  1.41000         0.8360   409.0     444.0  578.0
## 3      3 0.818000  0.87600         0.7670   314.0     342.0  487.0
## 4      4 0.716000  0.57000         0.6390   252.0     274.0  420.0
## 5      5 0.492000  0.36100         0.3680   207.0     225.0  367.0
## 6      6 0.257000  0.21500         0.1590   174.0     188.0  324.0
## 7      7 0.057500  0.09630         0.0361   148.0     159.0  289.0
## 8      8 0.000405  0.00925         0.0542   128.0     135.0  259.0
## 9      9 0.037200 -0.09300         0.1300   111.0     118.0  234.0
## 10    10 0.121000 -0.17700         0.3040    97.4     101.0  212.0
## 11    12 0.245000 -0.29600         0.5140    76.2      76.7  177.0
## 12    14 0.353000 -0.40800         0.6580    60.9      59.9  151.0
## 13    16 0.435000 -0.51100         0.7450    49.4      46.9  130.0
## 14    18 0.499000 -0.61100         0.8120    40.7      37.5  113.0
## 15    20 0.536000 -0.67700         0.8560    33.9      30.2   99.4
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
 softPower()  # soft power curve 

  modulePlot()  # plot modules  

  listWGCNA.Modules.out = listWGCNA.Modules() #modules
 input_selectGO5 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO5,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_selectWGCNA.Module <- 'Entire network'   #Select a module
 input_topGenesNetwork <- 10    #SoftPower to cutoff
 input_edgeThreshold <- 0.4 #Number of top genes 
 moduleNetwork()    # show network of top genes in selected module
##  softConnectivity: FYI: connecitivty of genes with less than 12 valid samples will be returned as NA.
##  ..calculating connectivities..

 input_removeRedudantSets <- TRUE   #Remove redundant gene sets 
 results <- networkModuleGO()  #Enrichment analysis of selected module
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.4e-83 100 Photosynthesis
2.7e-55 171 Oxidation-reduction process
1.9e-50 58 Photosynthesis, light reaction
9.4e-40 83 Generation of precursor metabolites and energy
1.8e-31 167 Response to abiotic stimulus
7.1e-26 27 Photosynthetic electron transport chain
1.4e-25 46 Electron transport chain
3.5e-24 43 Drug catabolic process
6.1e-24 82 Drug metabolic process
8.5e-24 24 Protein-chromophore linkage